问题描述：

让购房者描述他们梦想中的房子，他们可能不会从地下室的天花板的高度或者是距离铁路的距离来考虑。但是这个数据集可以包含了影响房价的因素。79 个解释变量（几乎）描述了爱荷华州艾姆斯住宅的各个方面，

数据集下载：

House Prices - Advanced Regression Techniques | Kaggle

实现代码：

读取数据文件和对数据处理

import numpy as npimport pandas as pdimport torch from torch import nn from d2l import torch as d2ltrain_data = pd.read_csv('datasets/house-prices/train.csv')test_data = pd.read_csv('datasets/house-prices/test.csv')#查看前四個和最後兩個特徵，以及相應標籤train_data.iloc[0:4,[0,1,2,3,-3,-2,-1]]#数据集中的id需要删除all_features = pd.concat((train_data.iloc[:,1:-1],test_data.iloc[:,1:]))all_features

数据预处理：

将缺失值替换为平均值

离散值用独热编码来替换

数据转换为张量，为训练做准备

'''若無法獲得測試數據，則可根據訓練數據計算平均值和標準差'''numeric_features = all_features.dtypes[all_features.dtypes != 'object'].indexall_features[numeric_features] = all_features[numeric_features].apply(lambda x:(x - x.mean()) / (x.std()))#在標準化數據後，所有均值消失，因此可以將缺失值設置為0all_features[numeric_features] = all_features[numeric_features].fillna(0)'''处理离散值。这包括诸如“MSZoning”之类的特征。我们用独热编码替换它们'''# “Dummy_na=True”将“na”(缺失值)视为有效的特征值，并为其创建指示符特征 all_features = pd.get_dummies(all_features, dummy_na=True) all_features.shape'''转换为张量'''n_train = train_data.shape[0]train_features = torch.tensor(all_features[:n_train].values, dtype=torch.float32) test_features = torch.tensor(all_features[n_train:].values, dtype=torch.float32) train_labels = torch.tensor(train_data.SalePrice.values.reshape(-1, 1), dtype=torch.float32)

定义模型和定义损失函数

loss = nn.MSELoss()in_features = train_features.shape[1]def get_model():model = nn.Sequential(nn.Linear(in_features,1)) return model'''預測價格的對數與真實標籤之間的差值'''def log_rmse(model,features,labels):clipp_preds = torch.clamp(model(features),1,float('inf'))rmse = torch.sqrt(loss(torch.log(clipp_preds),torch.log(labels)))return rmse.item()

训练函数（Adam优化器）

def train(model,train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):train_ls ,test_ls = [],[]train_iter = d2l.load_array((train_features,train_labels),batch_size)optimizer = torch.optim.Adam(model.parameters(),lr= learning_rate,weight_decay=weight_decay)for epoch in range(num_epochs):for x ,y in train_iter:optimizer.zero_grad()l = loss(model(x),y)l.backward()optimizer.step()train_ls.append(log_rmse(model,train_features,train_labels))if test_labels is not None:test_ls.append(log_rmse(model,test_features,test_labels))return train_ls,test_ls

验证函数（K折交叉验证）

'''k折交叉驗證'''def get_k_fold_data(k,i,x,y):assert k > 1fold_size = x.shape[0] // kx_train,y_train = None,Nonefor j in range(k):idx = slice(j * fold_size, (j+1) * fold_size)x_part,y_part = x[idx,:],y[idx]if j == i:x_valid,y_valid = x_part,y_partelif x_train is None :x_train,y_train = x_part,y_partelse :x_train = torch.cat([x_train,x_part],0)y_train = torch.cat([y_train,y_part],0)return x_train,y_train,x_valid,y_valid

返回训练和验证误差的平均值

def k_fold(k,x_train, y_train, num_epochs, learning_rate, weight_decay, batch_size):train_l_sum , valid_l_sum = 0,0for i in range(k):data = get_k_fold_data(k,i,x_train,y_train)model = get_model()train_ls, valid_ls = train(model, *data, num_epochs, learning_rate,weight_decay, batch_size)train_l_sum += train_ls[-1]valid_l_sum += valid_ls[-1]if i == 0:d2l.plot(list(range(1, num_epochs + 1)), [train_ls, valid_ls], xlabel='epoch', ylabel='rmse', xlim=[1, num_epochs], legend=['train', 'valid'], yscale='log' )print(f'折{i + 1} 训练log rmse{float(train_ls[-1]):f}, ' f'验证log rmse{float(valid_ls[-1]):f}')return train_l_sum / k, valid_l_sum / k

模型训练与可视化

'''模型選擇'''k, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 0, 64train_l, valid_l = k_fold(k, train_features, train_labels, num_epochs, lr,weight_decay, batch_size) print(f'{k}-折验证: 平均训练log rmse: {float(train_l):f}, 'f'平均验证log rmse: {float(valid_l):f}')

模型预测以及生成预测新文件

'''提交預測'''def train_and_pred(train_features, test_feature, train_labels, test_data, num_epochs, lr, weight_decay, batch_size):net = get_model()train_ls, _ = train(net, train_features, train_labels, None, None,num_epochs, lr, weight_decay, batch_size) d2l.plot(np.arange(1, num_epochs + 1), [train_ls], xlabel='epoch',ylabel='log rmse', xlim=[1, num_epochs], yscale='log') print(f'训练log rmse:{float(train_ls[-1]):f}')# 将网络应用于测试集。preds = net(test_features).detach().numpy()# 将其重新格式化以导出到Kaggletest_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1) submission.to_csv('submission.csv', index=False)'''生成文件'''train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size)

参考：

《动手学深度学习》